I'm finding code falls into two categories. Code that produces known results and code that produces results that are not known. For example, creating a table with a pagination component with a backend that loads the first 30 rows ordered by date descending from the database on page 1 and the second set of 30 rows on page 2. We know what the code is supposed to output, we know what the result looks like. On the other hand, there is code that does statistical analysis on the 30 rows of data. This is different because we don't know what the result is.
The known result code is easy to use an LLM with. I have a skill that will iterate with an OODA loop — observe, act, and validate. It will in the validate step take screenshots and even without telling it, it will query the database from the CLI, compare the rendered row data to the database data. It will more surprisingly make sure that all the components are responsive and render beautifully on mobile. I'm orders of magnitude past linting here which is solved with Biome.
The statistical analysis is different. The only way I can know for sure of the result is by writing the code painstakingly by hand. The LLM will always produce specious lies. It will fabricate and show me what I want to see, not the truth. This is because until it is written manually by hand, there is no ground truth. In this case, there is no code checking code.
At what point does this just wrap all the way back around to being genetic algorithms?
I'm also reminded of the old software called Formulize, which could take in a set of arbitrary data and find a function that described it. http://nutonian.wikidot.com/
> The Solution is Code-Checking Code
I'm finding code falls into two categories. Code that produces known results and code that produces results that are not known. For example, creating a table with a pagination component with a backend that loads the first 30 rows ordered by date descending from the database on page 1 and the second set of 30 rows on page 2. We know what the code is supposed to output, we know what the result looks like. On the other hand, there is code that does statistical analysis on the 30 rows of data. This is different because we don't know what the result is.
The known result code is easy to use an LLM with. I have a skill that will iterate with an OODA loop — observe, act, and validate. It will in the validate step take screenshots and even without telling it, it will query the database from the CLI, compare the rendered row data to the database data. It will more surprisingly make sure that all the components are responsive and render beautifully on mobile. I'm orders of magnitude past linting here which is solved with Biome.
The statistical analysis is different. The only way I can know for sure of the result is by writing the code painstakingly by hand. The LLM will always produce specious lies. It will fabricate and show me what I want to see, not the truth. This is because until it is written manually by hand, there is no ground truth. In this case, there is no code checking code.
At what point does this just wrap all the way back around to being genetic algorithms?
I'm also reminded of the old software called Formulize, which could take in a set of arbitrary data and find a function that described it. http://nutonian.wikidot.com/
soon